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Modelling phenology using ordered categorical generalized additive models
David L Miller
https://arxiv.org/abs/2508.07789 https://arxiv.org/pdf/2508.07789

Modelling phenology using ordered categorical generalized additive models
One form of data collected in ecology is phenological, describing the timing of life stages. It can be tempting to analyze such data using a continuous distribution or to model individual transitions via probit/logit models. Such simplifications can lead to incorrect inference in various ways, all of which stem from ignoring the natural structure of the data. This paper presents a flexible approach to modelling ordered categorical data using the popular R package `mgcv`. An example analysis of …

Variable selection via knockoffs in missing data settings with categorical predictors
Silvia Bacci, Emanuela Dreassi, Leonardo Grilli, Carla Rampichini
https://arxiv.org/abs/2508.06138

Variable selection via knockoffs in missing data settings with categorical predictors
Large-scale assessment data typically include numerous categorical variables, often affected by missing values. Motivated by the challenges arising in this framework, we extend the knockoffs method for selecting predictors to settings with missing values. Our proposal relies on a preliminary phase consisting of multiple imputations of missing values. Each imputed dataset is then processed using a suitable knockoff filter. We evaluate the performance of the proposed method through a simulation s…

Augmented Shuffle Differential Privacy Protocols for Large-Domain Categorical and Key-Value Data
Takao Murakami, Yuichi Sei, Reo Eriguchi
https://arxiv.org/abs/2509.02004 https:…

Augmented Shuffle Differential Privacy Protocols for Large-Domain Categorical and Key-Value Data
Shuffle DP (Differential Privacy) protocols provide high accuracy and privacy by introducing a shuffler who randomly shuffles data in a distributed system. However, most shuffle DP protocols are vulnerable to two attacks: collusion attacks by the data collector and users and data poisoning attacks. A recent study addresses this issue by introducing an augmented shuffle DP protocol, where users do not add noise and the shuffler performs random sampling and dummy data addition. However, it focuse…

STRive: An association rule-based system for the exploration of spatiotemporal categorical data
Mauro Diaz, Luis Sante, Joel Perca, Jo\~ao Victor da Silva, Nivan Ferreira, Jorge Poco
https://arxiv.org/abs/2509.02732

STRive: An association rule-based system for the exploration of spatiotemporal categorical data
Effectively analyzing spatiotemporal data plays a central role in understanding real-world phenomena and informing decision-making. Capturing the interaction between spatial and temporal dimensions also helps explain the underlying structure of the data. However, most datasets do not reveal attribute relationships, requiring additional algorithms to extract meaningful patterns. Existing visualization tools often focus either on attribute relationships or spatiotemporal analysis, but rarely supp…

Clustering methods for Categorical Time Series and Sequences : A scoping review
Ottavio Khalifa, Viet-Thi Tran, Alan Balendran, Fran\c{c}ois Petit
https://arxiv.org/abs/2509.07885

Clustering methods for Categorical Time Series and Sequences : A scoping review
Objective: To provide an overview of clustering methods for categorical time series (CTS), a data structure commonly found in epidemiology, sociology, biology, and marketing, and to support method selection in regards to data characteristics. Methods: We searched PubMed, Web of Science, and Google Scholar, from inception up to November 2024 to identify articles that propose and evaluate clustering techniques for CTS. Methods were classified according to three major families -- distance-based,…

Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization
MD Shaikh Rahman, Feiroz Humayara, Syed Maudud E Rabbi, Muhammad Mahbubur Rashid
https://arxiv.org/abs/2508.04790

Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization
Content-based mammographic image retrieval systems require exact BIRADS categorical matching across five distinct classes, presenting significantly greater complexity than binary classification tasks commonly addressed in literature. Current medical image retrieval studies suffer from methodological limitations including inadequate sample sizes, improper data splitting, and insufficient statistical validation that hinder clinical translation. We developed a comprehensive evaluation framework sy…

Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classification
Jake S. Rhodes, Adam G. Rustad, Sofia Pelagalli Maia, Evan Thacker, Hyunmi Choi, Jose Gutierrez, Tatjana Rundek, Ben Shaw
https://arxiv.org/abs/2509.22919

Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classification
Missing data is a common problem in time series data. Most methods for imputation ignore label information pertaining to the time series even if that information exists. In this paper, we provide a framework for missing data imputation in the context of time series classification, where each time series is associated with a categorical label. We define a means of imputing missing values conditional upon labels, the method being guided by powerful, existing supervised models designed for high ac…

Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training
Xin Zhang, Quanyu Zhu, Liangbei Xu, Zain Huda, Wang Zhou, Jin Fang, Dennis van der Staay, Yuxi Hu, Jade Nie, Jiyan Yang, Chunzhi Yang
https://arxiv.org/abs/2508.03854

Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training
The increasing complexity of deep learning recommendation models (DLRM) has led to a growing need for large-scale distributed systems that can efficiently train vast amounts of data. In DLRM, the sparse embedding table is a crucial component for managing sparse categorical features. Typically, these tables in industrial DLRMs contain trillions of parameters, necessitating model parallelism strategies to address memory constraints. However, as training systems expand with massive GPUs, the tradi…

Test-Time Anchoring for Discrete Diffusion Posterior Sampling
Litu Rout, Andreas Lugmayr, Yasamin Jafarian, Srivatsan Varadharajan, Constantine Caramanis, Sanjay Shakkottai, Ira Kemelmacher-Shlizerman
https://arxiv.org/abs/2510.02291

Test-Time Anchoring for Discrete Diffusion Posterior Sampling
We study the problem of posterior sampling using pretrained discrete diffusion foundation models, aiming to recover images from noisy measurements without retraining task-specific models. While diffusion models have achieved remarkable success in generative modeling, most advances rely on continuous Gaussian diffusion. In contrast, discrete diffusion offers a unified framework for jointly modeling categorical data such as text and images. Beyond unification, discrete diffusion provides faster i…

Bayesian Estimation and Regularization Techniques in Categorical Data Analysis
Jan Kalina
https://arxiv.org/abs/2509.02222 https://arxiv.org/pdf/2509.02222…

Bayesian Estimation and Regularization Techniques in Categorical Data Analysis
This paper explores Bayesian estimation for categorical data, focusing on simple yet effective models that provide a foundation for applying more advanced methods accurately and reliably in real-world applications. We begin by revisiting Bayesian estimators for the binomial distribution and investigating their properties. Next, we develop hypothesis tests for categorical data (sign test, homogeneity test, symmetry test) based on regularized maximum likelihood estimates of the probabilities. Fin…

Unsupervised Learning and Representation of Mandarin Tonal Categories by a Generative CNN
Kai Schenck, Ga\v{s}per Begu\v{s}
https://arxiv.org/abs/2509.17859 https://

Unsupervised Learning and Representation of Mandarin Tonal Categories by a Generative CNN
This paper outlines the methodology for modeling tonal learning in fully unsupervised models of human language acquisition. Tonal patterns are among the computationally most complex learning objectives in language. We argue that a realistic generative model of human language (ciwGAN) can learn to associate its categorical variables with Mandarin Chinese tonal categories without any labeled data. All three trained models showed statistically significant differences in F0 across categorical varia…

Human Activity Recognition Based on Electrocardiogram Data Only
Sina Montazeri, Waltenegus Dargie, Yunhe Feng, Kewei Sha
https://arxiv.org/abs/2509.19328 https://

Human Activity Recognition Based on Electrocardiogram Data Only
Human activity recognition is critical for applications such as early intervention and health analytics. Traditional activity recognition relies on inertial measurement units (IMUs), which are resource intensive and require calibration. Although electrocardiogram (ECG)-based methods have been explored, these have typically served as supplements to IMUs or have been limited to broad categorical classification such as fall detection or active vs. inactive in daily activities. In this paper, we ad…

Detecting distinctive structural changes in economic data
Joanna D\k{e}bicka, Edyta Mazurek
https://arxiv.org/abs/2509.02712 https://arxiv.org/pdf/2509.027…

Detecting distinctive structural changes in economic data
This article introduces a novel method for detecting distinctive structural changes in economic data, particularly within frequency distribution tables. The approach identifies significant shifts in the distribution of a variable over time or across populations, capturing changes in category shares, enabling a deeper understanding of the underlying dynamics and trends. The method is applicable to both categorical and numerical data and is especially useful in fields such as industrial economics…

Characterizing Visualization Perception with Psychological Phenomena: Uncovering the Role of Subitizing in Data Visualization
Arran Zeyu Wang, Ghulam Jilani Quadri, Mengyuan Zhu, Chin Tseng, Danielle Albers Szafir
https://arxiv.org/abs/2508.17460

Characterizing Visualization Perception with Psychological Phenomena: Uncovering the Role of Subitizing in Data Visualization
Understanding how people perceive visualizations is crucial for designing effective visual data representations; however, many heuristic design guidelines are derived from specific tasks or visualization types, without considering the constraints or conditions under which those guidelines hold. In this work, we aimed to assess existing design heuristics for categorical visualization using well-established psychological knowledge. Specifically, we examine the impact of the subitizing phenomenon …

Agent0: Leveraging LLM Agents to Discover Multi-value Features from Text for Enhanced Recommendations
Bla\v{z} \v{S}krlj, Beno\^it Guilleminot, Andra\v{z} Tori
https://arxiv.org/abs/2507.18993

Agent0: Leveraging LLM Agents to Discover Multi-value Features from Text for Enhanced Recommendations
Large language models (LLMs) and their associated agent-based frameworks have significantly advanced automated information extraction, a critical component of modern recommender systems. While these multitask frameworks are widely used in code generation, their application in data-centric research is still largely untapped. This paper presents Agent0, an LLM-driven, agent-based system designed to automate information extraction and feature construction from raw, unstructured text. Categorical f…

The Classification of 3 1d Symmetry Enriched Topological Order
Thibault D. D\'ecoppet, Matthew Yu
https://arxiv.org/abs/2509.10603 https://arxiv.org/pd…

The Classification of 3+1d Symmetry Enriched Topological Order
We use a 2-categorical version of (de-)equivariantization to classify (3+1)d topological orders with a finite $G$-symmetry. In particular, we argue that (3+1)d fermionic topological order with $G$-symmetry correspond to $\mathbf{2SVect}$-enriched $G$-crossed braided fusion 2-categories. We then show that the categorical data necessary to define these theories agrees with that arising from a fermionic generalization of the Wang-Wen-Witten construction of bosonic topological theories with $G$-sym…

Compositional System Dynamics: The Higher Mathematics Underlying System Dynamics Diagrams & Practice
Xiaoyan Li, Evan Patterson, Patricia L. Mabry, Nathaniel D. Osgood
https://arxiv.org/abs/2509.18475

Compositional System Dynamics: The Higher Mathematics Underlying System Dynamics Diagrams & Practice
This work establishes a robust mathematical foundation for compositional System Dynamics modeling, leveraging category theory to formalize and enhance the representation, analysis, and composition of system models. Here, System Dynamics diagrams, such as stock & flow diagrams, system structure diagrams, and causal loop diagrams, are formulated as categorical constructs, enabling scalable, transparent, and systematic reasoning. By encoding these diagrams as data using attributed C-sets and utili…

GraphCSVAE: Graph Categorical Structured Variational Autoencoder for Spatiotemporal Auditing of Physical Vulnerability Towards Sustainable Post-Disaster Risk Reduction
Joshua Dimasaka, Christian Gei{\ss}, Robert Muir-Wood, Emily So
https://arxiv.org/abs/2509.10308

GraphCSVAE: Graph Categorical Structured Variational Autoencoder for Spatiotemporal Auditing of Physical Vulnerability Towards Sustainable Post-Disaster Risk Reduction
In the aftermath of disasters, many institutions worldwide face challenges in continually monitoring changes in disaster risk, limiting the ability of key decision-makers to assess progress towards the UN Sendai Framework for Disaster Risk Reduction 2015-2030. While numerous efforts have substantially advanced the large-scale modeling of hazard and exposure through Earth observation and data-driven methods, progress remains limited in modeling another equally important yet challenging element o…

Calibration offset estimation in mobile hearing tests via categorical loudness scaling
Chen Xu, Birger Kollmeier
https://arxiv.org/abs/2508.14824 https://a…

Calibration offset estimation in mobile hearing tests via categorical loudness scaling
Objective: To enable reliable smartphone-based hearing assessments by developing methods to estimate device calibration offsets using categorical loudness scaling (CLS). Design: Calibration offsets were simulated from a Gaussian distribution. Two prediction models - a Bayesian regression model and a nearest neighbor model - were trained on CLS-derived parameters and data from the Oldenburg Hearing Health Repository (OHHR). CLS was chosen because it provides level-independent measures (e.g., dyn…

Bi-dendrograms for clustering the categories of a multivariate categorical data set
Michael Greenacre, Maurizio Vichi
https://arxiv.org/abs/2509.15939 https://

Bi-dendrograms for clustering the categories of a multivariate categorical data set
The clustering of categories in a multivariate categorical data set is investigated, where the problem separates into that of merging categories of the same variables (i.e., within-variable categories), and combining categories of different variables (i.e., between-variable categories). For the within-variable problem, the objective is to arrive at fewer categories (and, consequently, lower data dimensionality) without affecting the essential features of the data set, thereby simplifying the in…

Real-World Summarization: When Evaluation Reaches Its Limits
Patr\'icia Schmidtov\'a, Ond\v{r}ej Du\v{s}ek, Saad Mahamood
https://arxiv.org/abs/2507.11508

Real-World Summarization: When Evaluation Reaches Its Limits
We examine evaluation of faithfulness to input data in the context of hotel highlights: brief LLM-generated summaries that capture unique features of accommodations. Through human evaluation campaigns involving categorical error assessment and span-level annotation, we compare traditional metrics, trainable methods, and LLM-as-a-judge approaches. Our findings reveal that simpler metrics like word overlap correlate surprisingly well with human judgments (Spearman correlation rank of 0.63), often…

Modelling longitudinal polytomous animal data using Bayesian hierarchical models
Maria Let\'icia Salvador, Gabriel Rodrigues Palma, Mariana Coelly Modesto Santos Tavares, Iran Jose Oliveira Silva, Idemauro Antonio Rodrigues de Lara
https://arxiv.org/abs/2507.17004

Modelling longitudinal polytomous animal data using Bayesian hierarchical models
The analysis of longitudinal categorical data can be complex and unfeasible due to the number of parameters involved, characterised by overparameterisation leading to model non-convergence, in addition to problems related to sample size and the presence or absence of overdispersion. In this context, we introduce Bayesian hierarchical models as an alternative methodology to classical statistical techniques for analysing nominal polytomous data in longitudinal studies. The theoretical foundation …

Bayesian Variational Inference for Mixed Data Mixture Models
Junyang Wang, James Bennett, Victor Lhoste, Sarah Filippi
https://arxiv.org/abs/2507.16545 htt…

Bayesian Variational Inference for Mixed Data Mixture Models
Heterogeneous, mixed type datasets including both continuous and categorical variables are ubiquitous, and enriches data analysis by allowing for more complex relationships and interactions to be modelled. Mixture models offer a flexible framework for capturing the underlying heterogeneity and relationships in mixed type datasets. Most current approaches for modelling mixed data either forgo uncertainty quantification and only conduct point estimation, and some use MCMC which incurs a very high…

Topological Sequence Analysis of Genomes: Category Approaches
Jian Liu, Li Shen, Mushal Zia, Guo-Wei Wei
https://arxiv.org/abs/2507.08043 https://

Topological Sequence Analysis of Genomes: Category Approaches
Sequence data, such as DNA, RNA, and protein sequences, exhibit intricate, multi-scale structures that pose significant challenges for conventional analysis methods, particularly those relying on alignment or purely statistical representations. In this work, we introduce category-based topological sequence analysis (CTSA ) of genomes. CTSA models a sequence as a resolution category, capturing its hierarchical structure through a categorical construction. Substructure complexes are then derived …

Fiducial Matching: Differentially Private Inference for Categorical Data
Ogonnaya Michael Romanus, Younes Boulaguiem, Roberto Molinari
https://arxiv.org/abs/2507.11762

Fiducial Matching: Differentially Private Inference for Categorical Data
The task of statistical inference, which includes the building of confidence intervals and tests for parameters and effects of interest to a researcher, is still an open area of investigation in a differentially private (DP) setting. Indeed, in addition to the randomness due to data sampling, DP delivers another source of randomness consisting of the noise added to protect an individual's data from being disclosed to a potential attacker. As a result of this convolution of noises, in many cases…

Multinomial thresholded LASSO for interpretable dimension reduction of human activity sequences
Zuofu Huang, Yingling Fan, James Hodges, Julian Wolfson
https://arxiv.org/abs/2507.17900

Multinomial thresholded LASSO for interpretable dimension reduction of human activity sequences
The widespread collection of data from mobile and wearable devices has created unprecedented opportunities to study human behavior in fine temporal resolution. One common structure for such data is categorical sequences: ordered, multinomial observations across many time points. These sequences present unique statistical challenges due to their high dimensionality and complex temporal dependence, including both short- and long-term correlations. Yet, there has been relatively little methodologi…

Learning Discrete Bayesian Networks with Hierarchical Dirichlet Shrinkage
Alexander Dombowsky, David B. Dunson
https://arxiv.org/abs/2509.13267 https://arx…

Learning Discrete Bayesian Networks with Hierarchical Dirichlet Shrinkage
Discrete Bayesian networks (DBNs) provide a broadly useful framework for modeling dependence structures in multivariate categorical data. There is a vast literature on methods for inferring conditional probabilities and graphical structure in DBNs, but data sparsity and parametric assumptions are major practical issues. In this article, we detail a comprehensive Bayesian framework for learning DBNs. First, we propose a hierarchical prior for the conditional probabilities that enables complicate…